Paper from Meta shows Quantized reasoning models often lose because they keep doubting a correct answer instead of finishing.

Many of them reason well enough， but compression makes them hesitate at the wrong time.

The problem is that post-training quantization， a way to shrink models after training， can make reasoning models cheaper to run but worse at finishing cleanly.

The authors found that strong quantization does not only make models less capable， since in many failures the model already reached the right answer but then second-guessed itself.

Their core idea is that quantization adds noise at uncertain word choices， so the model becomes more likely to pick words like "wait，" "but，" or "alternatively" that reopen the problem.